Financial Risk Management on a Neutral Atom Quantum Processor

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Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, combined with classical algorithms, may deliver competitive, faster and more interpretable models. Lucas Leclerc, Research and Development Engineer at PASQAL, and his team of fellow researchers proposes a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. Leclerc and team implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. They report competitive performances against the state-of-the-art Random Forest Benchmark with their model achieving better interpretability and comparable training times. Leclerc and team examine how to improve performance in the near-term, validating their ideas with Tensor Networks-based numerical simulations.